Effective security scheduler

ABSTRACT

The present invention provides a system and process for creating an effective work schedule for a security checkpoint. The process includes the step of analyzing passenger flow to determine the coverage needed to sustain required service levels, generally through simulating the checkpoint to determine required staffing levels. Another step to optimize workforce levels and schedules is to create workforce schedules that are based on optimized person-hours and key variables. The schedule staffs as needed to achieve the required staffing levels and may consider numerous other factors, including acceptable ranges for shift lengths; a maximum number of start times; and a percentage of part-time or seasonal employees. In a particular embodiment, the schedule is formed by using linear programming to solve for a tour assignment matrix (defining a schedule) from a demand matrix representing the needed number of workers and a co efficient matrix representing the availability of workers.

This is a continuation of application Ser. No. 10/400,441, filed Mar.28, 2003, and now U.S. Pat. No. 7,840,435, which is incorporated hereinby reference.

FIELD OF THE INVENTION

The present invention relates to a system and method for the effectiveand efficient scheduling of personnel at a security checkpoint.Effective and efficient scheduling closely matches staffing levels withstaffing requirements while meeting personnel demands, staffingconstraints, and performance measures.

BACKGROUND OF THE INVENTION

In general, security staffing has been determined somewhat arbitrarily,without concern for the number of people being served by the securitystaff (i.e., the demand for security screening). Even when securityworkers are staffed in view of an estimated demand for securityscreening, the estimate is generally haphazardly formed and unreliable.

Staffing without accurate forecasting of the demand for securityscreening creates several potential problems. If a security checkpointis understaffed, the security checkpoint operates below optimalefficiency, potentially delaying people passing through the checkpoint.

Conversely, the overstaffing of security personnel leads in aninefficient condition in which some of the security personnel are idle,resulting in excess labor costs.

One difficulty in security staffing in view of the demand for securityscreening is that demand levels are difficult to forecast. Specifically,demand levels often vary greatly. For instance, the number of peopletraveling at airports or seaports varies wildly, causing demand forsecurity screening in these locations to vary correspondingly.Similarly, the number of people entering a public venue varies as eventsapproach. Furthermore, customer behavior can differ greatly, dependingon the location, the event, the time, etc.

Accordingly, the number of security workers should closely match thestaffing levels needed for the public's demand for security screening.However, this type of staffing is quite difficult to implement sincesecurity workers cannot be assigned instantaneously in desiredquantities. For example, each of the security workers is employed eachday for a shift of fixed duration (generally 8 hours), a fixed number ofshifts staffing period (typically five days per week). Also, securityworkers may not accept fluctuating starting and ending times.

Furthermore, employment rules and security regulations place limitationson staffing, such as a regulation may require that checkpoint workerswork five or less consecutive days and must receive at least two restdays per week.

SUMMARY OF THE INVENTION

Thus, it is the goal of the present invention to provide a tactical toolthat has the flexibility to incorporate the unique characteristics ofany security checkpoint and to forecast the number of personnelnecessary to assure that desired service levels are met.

Another goal of the present invention is to provide an operational toolthat gives the ability for a security checkpoint to optimize workers'schedules based on demand and individuals' qualifications. Thiscapability further assures that business rules such as breaks andqualifications are met.

It is a further goal of the present invention to provide a strategictool enabling decision makers to understand the cost and performanceimpacts of alternative policies. In this way, the present inventioncould provide flexibility to shape policy based on information, not justintuition.

Another goal of the present invention to forecast security staffingneeds over an extended period and to staff security personnel accordingto the forecasted needs. In this way, security checkpoint management maymatch seasonal demand fluctuations using hiring lead times and managedattrition to enable staffing over an extended period as needed to meetdesired service levels. The modeling of demand at different times andthe appropriate staffing according to these demands should occur duringor before a budget cycle so that the management of a security checkpointmay anticipate casts and plan accordingly.

In response to these and other needs, the present invention provides asystem and method for effectively staffing security workers. Inaccordance with an embodiment of the present invention, the methodincludes the steps of forecasting security screening demand at differenttimes, determining the number of security employees needed to meet theestimated demand for security screening according to securityperformance concerns, and then forming a work schedule that staffs theneeded number of security employees. In other embodiments of the presentinvention, the effective security scheduling method may further includethe steps of implementing the proposed security schedule and adjustingthe security scheduler in accordance with employee requests and resultsfrom previously implemented schedules.

In a particular implementation, the present invention has specificapplication to staffing security checkpoints. In this embodiment, thenumber of needed open stations in security checkpoints is determined bytranslating the variable demand for security at different times andusing linear programming to optimize and determine a schedule as neededto staff the needed number of open stations.

In another embodiment, the present invention may be used to provide astrategic tool enabling decision makers to understand the cost andperformance impacts of alternative directives and general policies. Theinvention may be used to compare optimal staffing cost with and withoutthe implementation of a security directive or policy. Specifically, thepresent invention may be used to determine the change in staffing costsfrom modifications to work schedules necessitated by the implementationof the security directive or policy.

In another embodiment, the present invention provides a software-basedapplication for effectively scheduling security personnel. Thesoftware-based application includes a module for modeling the securitycheckpoint to forecast security screening demand at different times andto determine the number of security employees needed to meet theforecasted demand. The software-based application further includes amodule for forming a work schedule that staffs the forecasted need forsecurity personnel. The software-based application may further includemodules for implementing the work schedule and for modifying the workschedule as needed.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other advantages of the present invention are described morefully in the following drawings and accompanying text in which likereference numbers represent corresponding parts throughout:

FIGS. 1A-1B are exemplary charts depicting the relationship betweensecurity staffing and security capacity in accordance with embodimentsof the present invention;

FIGS. 2A-2C are exemplary charts depicting the relationship betweenactual security staffing and demand for security staffing in accordancewith embodiments of the present invention;

FIGS. 3, 4A-4D, and 8A-8C are flowcharts depicting the steps in a methodfor effective security scheduling in accordance with embodiments of thepresent invention;

FIGS. 5 and 6A-6B are exemplary charts depicting security demand inaccordance with embodiments of the present invention;

FIG. 7A is an exemplary chart depicting the relationship security demandand a corresponding number of workers needed to accommodate thatsecurity demand in accordance with an embodiment of the presentinvention;

FIG. 7B is an exemplary chart depicting the relationship betweensecurity demand and a corresponding number of security stations in thecheckpoint needed to accommodate that security demand in accordance withan embodiment of the present invention;

FIG. 9 is a schematic diagram of a system for implementing the method ofFIG. 3 in accordance with embodiments of the present invention; and

FIG. 10 is a chart depicting exemplary changes to the number of neededsecurity workers over an extended period.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

The present invention generally provides a system and method of staffingsecurity workers as needed to accommodate demand for security screening.Demand for security screening varies according to the number of peopleneeding security at a location. Generally, as more people enter alocation, more security officials are required at that location in orderto provide a desired service level. While the remainder of thisapplication refers specifically to staffing at a security checkpoint, itshould be appreciated that the present invention may be used to staffany security officials, regardless of their post. If used to staffsecurity workers outside of security checkpoints, the present inventionmay use different models for estimating security screening demand. Thestaffing of security workers outside of the checkpoint is described ingreater detail below.

FIG. 1A is a capacity-to-station graph 100 that depicts the number ofpeople that can pass through a checkpoint as a function of the number ofstations in the checkpoint. Each of the stations operates separately. Ascan be expected, the capacity of the checkpoint is approximately alinear function of the number of stations since the security stationsfunction independently. Thus, the capacity of a checkpoint may bemodified as needed by adjusting the number of security stations. In thiscontext, a partial station is incompletely staffed, thereby operatingbelow optimal efficiency. The partial staffing of a station is describedin greater detail below. The capacity-to-station graph 100 is roughlyshaped as a step function because the checkpoint capacity increases withthe opening of each additional security station.

Each of the stations is separately staffed with a number of employees asneeded. For instance, a security station may use five employees, eachmanning a component of the security station (a walk-through metaldetector, an x-ray machine, a hand-held metal detector, a station tomanually search personal belongings, and an area to perform othersecurity tests). Obviously, any number of people may be staffed to astation. A station may also be partially staffed, operating a lowerlevel of throughput as the security workers are required to perform morethan one function. Furthermore, additional workers may be staffed to asecurity checkpoint to improve the throughput of that station. In thisway, the capacity of security checkpoints generally corresponds to thenumber of security workers staffed at the security stations.

Corresponding to the capacity-to-station graph 100 from FIG. 1A, FIG. 1Bdepicts a demand-to-employee graph 110 that represents the number ofpeople that can pass through one or more checkpoints as a function ofthe number of workers at the checkpoints. Thus, the demand of acheckpoint may be met as needed by adjusting the number of employees atthe security stations as described above.

As described above, a non-optimized work schedule may result ininefficient staffing levels. Turning now to FIG. 2A, a non-optimizedscheduling chart 200 represents a uniform staffing level, represented bythe number of staffed workers 220, that does not vary with changes inthe number of needed workers 210. For instance, the number of staffedworkers 220 may represent the average number of needed security workers.At times, such as the time periods around 6 AM and 6 PM, the securitycheckpoint is understaffed, in that the number of needed workers exceedsthe number of staffed workers. In this situation, long lines may form atthe security checkpoint. Conversely, the security checkpoint may also beoverstaffed at times (such as the time period around 12 PM). The excesscapacity results in inefficient labor allocation and unnecessary laborcosts. The non-optimized scheduling chart 200 graphically depicts theoverstaffing as the difference between the number of required employees,line 210, and the number of employees working, line 220.

In most checkpoints, security workers are typically staffed using blockscheduling. FIG. 2B. schematically represents a block scheduling schemein which a certain number of security workers are employed from 12 AM to12 PM, and a second number of security workers are employed from 12 PMto 12 AM. It should be appreciated that most checkpoints are not staffedin twelve-hour blocks, and that this example is provided merely forillustration. In FIG. 2B, line 210′ represents the number of requiredsecurity employees (corresponding to FIG. 2A), and line 220′ representsthe number of security employees working in the twelve-hour blockscheduling scheme. With block scheduling, the security workers aretypically understaffed at times, and overstaffed at other times. Asdescribed above, overstaffing is inefficient and results in excessivelabor costs, while understaffing results in excessive labor costs, whileunderstaffing results in excessive delays as the security workers areunable to meet demand for security screening.

In response, the present invention provides a system and method for theeffective and efficient staffing of employees at the securitycheckpoint. The present invention operates by forecasting the demand forsecurity screening, determining the number of security stations in acheckpoint needed to satisfy this forecasted demand, and then creatingan effective work schedule that staffs as needed to achieve desiredperformance measures, and therefore effectively staffing securityworkers.

Turning to FIG. 3, one embodiment of the present invention is aneffective security scheduling (ESS) method 300. The ESS method 300includes the steps of: forecasting the number of security stations to beopen at the checkpoint at different time periods, step 400 and definingan optimized work schedule, step 800. The ESS method 300 may optionallyalso include the steps of implementing and analyzing the optimizedschedule, step 310; and adjusting the security work schedule as needed,step 320. Each of these steps is described in greater detail below.

Planning

In accordance with an embodiment of the present invention, a process forforecasting the number of workers needed at the security checkpoint atdifferent times, step 400, is illustrated in FIG. 4A. A first action instep 400 is to forecast security screening demand at that location, step410, by collecting demand data on the number of people passing throughthe security checkpoint. For instance, the number of people passingthrough a security checkpoint may be empirically determined throughmanual or mechanical counting.

Preferably, the demand data is automatically and dynamically determined,as illustrated in FIG. 4B. In the context of an airport or seaport, thenumber of passengers can be estimated by connecting to reservationsystems or to similar passenger record systems. Then, flight or shipschedules can be analyzed, step 411, to determine a total potentialnumber of passengers. This capacity of passengers may be multiplied by aload factor (i.e., the actual percentage of seats sold) in step 412 todetermine the actual number of passengers. This number is then adjustedfor the number of passengers transferring from previous flights, step413, to determine the number of passengers actually originating from theparticular location and, therefore, actually passing through thesecurity checkpoint. For example, if a flight has a capacity of 200passengers and if the load factor is 75% (¾), then 150 passengers shouldbe on the flight. Of these 150 passengers, if a third (⅓) hastransferred from other flights, then the remaining 100 passengers passthrough the security checkpoint at that airport.

Continuing with the airport scenario, a demand distribution curve (alsoknown as a check-in curve) may be created and used to determine theinstantaneous number of people passing through the security checkpoint,step 414. With passengers at an airport, the demand curve reflects thetime before departure that passengers arrive at the checkpoint.

FIG. 5 depicts an exemplary demand curve 500 representing the demandattributable to a single event at 6 PM, such as a flight or a publicevent. In curve 500, increasing numbers of people arrive at thecheckpoint before 6 PM, but the number of the people drops off rapidlythereafter.

The demand curve for each flight or event, such as demand curve 500depicted in FIG. 5, is then totaled, step 415, to calculate the totalnumber of people passing through a checkpoint at any particular time ortime period. FIG. 6A depicts an exemplary total demand curve 600 havingpeaks around 6 AM and 6 PM. Locations, such as airports, typically haveone or more peak periods during the day corresponding with periods ofhigh traffic. In the same way, a demand at a security checkpointgenerally vary over longer periods, with resulting peak days, peakweeks, etc.

The number of passengers arriving at the security checkpoint may bedivided into fixed time periods, such as 30-minute intervals. Theaverage demand during each of the periods may then be displayed, asillustrated in total demand curve 600′ in FIG. 6B, as the horizontalline in each of the boxes. The overall number of passengers during thetime period will be the area of the box, or the average demandmultiplied by the time period.

It should be appreciated that the above-described method for estimatingdemand at the security checkpoint, while presented in the context of anairport or seaport, may be used in a variety of circumstances. Forinstance, the above-described method may be used to determine securityscreening demand at a large volume event, such as a concert or sportscontest. The total number of people may then be estimated as the numberof ticket-holders minus forecasted non-attendance. The instantaneousdemand at the security checkpoint may then be determined at using ademand curve for the event.

Obviously, the demand for security screening may be adjusted for otherfactors. For instance, if the non-ticketed public is allowed within thelocation, then the demand should be adjusted for these additionalpeople. Similarly, a location may contain several checkpoints, and thedemand may be determined separately for each of the checkpoints or forthe entire location.

Using the demand data determined in step 410, the checkpoint may bemodeled using various modeling techniques, step 420. Process modeling iswell-known 3 technology, and various techniques may be used to produce amodel of the security checkpoint given the input data collected in step410.

The security checkpoint may be modeled in step 420 using a certainnumber of open stations. The security checkpoint is then modeled againusing a different number of open stations. The results from the twomodels may be compared to choose a desirable number of open stations.Typically, reducing the number of stations is detrimental to servicemeasures, such as waiting time, but reduced employment costs. In thisway, the model may then be used to provide a fact-based forecast of thevarying number of stations. It should be appreciated that the modelingof the security checkpoint does not schedule workers. Instead, the modelprovides an optimal number of open stations per time period as needed tomeet various service measures (and thus, the optimal number of securityworkers for each of the time periods). The actual staffing of thesecurity workers is described below.

Security checkpoints may be modeled and simulated in step 420, asdepicted in FIG. 4C, using a black-box security checkpoint model 2 thatreceives input data 1 and produces output data 3. The input data 1generally corresponds to the number of people la entering the securitycheckpoint. The output value 3 generally includes measurements ofcustomer experience (such as wait time, processing time, queue length,etc.) based on checkpoint demand, alarm rates, processing times,scheduled resources, and security policies.

The black-box security checkpoint model 2 functions as a black-boxhaving a set of possible output values and some type of rule forselecting from the set of possible output values. For example, outputdata 3 may include customer wait time in the security checkpoint, wherethe process or service time for security checkpoint model 2 may bebounded by a minimum and a maximum time, such as 10 and 100 seconds.Particular process, service or activity values for each simulated personmay be randomly assigned according to a statistical distribution, suchas uniform, normal, Poisson distributions, etc. The particular valuesand distribution used in the black-box-style security checkpoint model 2may be selected as necessary to conform to an actual securitycheckpoint. For instance, the actual process times at a securitycheckpoint may be measured to determine a minimum value, a maximumvalue, and a distribution of process times between these values. Thecustomary wait time is then a function of the process time and number ofresources in the checkpoint model.

In this way, the black-box-style security checkpoint model 2 aggregatestogether the individual tasks and processes occurring in the securitycheckpoint to determine output values. While the black-box-stylesecurity checkpoint model 2 illustrated in FIG. 4C is able to simulatean existing security checkpoint, this type of model has a limitedability to predict the effects of changes in the individual tasks andprocesses occurring in the checkpoint. Specifically, the black-box model2 does not match up resources to activities in the checkpoint. Whilesomeone may attempt to use the black-box model 2 to predict the effectsof changes by varying the output value ranges or the distribution of thevalues, the predictive accuracy of the black-box model 2 is generallypoor. In particular, the effects of changes in one or more of theindividual tasks and processes occurring in the security checkpoint arenot easily represented through the black-box model 2 because these theindividual tasks and processes are not separately replicated.

In a preferred embodiment of the present invention, the securitycheckpoint is modeled as described in co-owned U.S. patent applicationSer. No. 10/293,469 entitled SECURITY CHECKPOINT SIMULATION, thedisclosure of which is hereby incorporated by reference in full. U.S.patent application Ser. No. 10/293,469 provides a security checkpointmodel 10, as depicted in FIG. 4D, having two or more processes, such asentering the security checkpoint in step 11, screening items in step 12,and screening people in step 13. This security checkpoint model is moresimilar to an actual security checkpoint. Each of the steps 11, 12, and13 may be separately simulated to produce output values as describedabove. Thus, each of the steps 11, 12, and 13 may be separately modeledblack-boxes. For instance, a user may define rules for simulating outputvalues for each of the steps 11, 12, and 13. To model changes in thecheckpoint, the values or distribution for steps 11, 12, or 13 may beadjusted. By adjusting values for separate steps, the passengercheckpoint model 10 more accurately approximates changes in a passengercheckpoint.

One or more of the steps 11, 12, and 13 may be further decomposed intoone or more separate substeps. Then, each of the substeps of steps 11,12, and 13 may be separately modeled processes having user-defined rulesfor simulating output values, which are aggregated to produce totaloutput values for steps 11, 12, and 13.

In another embodiment, the security checkpoint model 10 may alsoconsider the effects of passenger check-in 14 on the passenger demandfor security screening, as further described in the above-cited U.S.patent application Ser. No. 10/293,469. In general, an extended check-inperiod serves to buffer the security demand. Specifically, the securitycheckpoint model 10 may be adapted to consider processes occurring in anairport before a passenger enters a security checkpoint. Typically,certain percentages of passengers check-in at various check-inlocations, such as curb check-in, counter check-in, or self-servecheck-in. These percentages are predetermined and may be selected asneeded, and if one of the check-in locations is not present in anairport of interest, its associated usage percentage may be set to zero.Alternatively, passengers may also choose to not check-in and insteadproceed directly to the security checkpoint.

During the check-in process in step 14, the passenger may also check-inbaggage, and a certain percentage of the baggage may then be screened.For instance, baggage may be screened using an Explosive DetectionSystem (EDS). The EDS tests baggage for explosives by scanning theinternal contents of baggage placed in the EDS. The percentage of thebags searched during check-in step 14 is predetermined and may bedefined as specified above. If there is no desire to simulate the EDS orother methods of screening checked-in baggage, the percentage ofpassengers affected by these processes may be set to zero. Similarly, ifthe airport safety rules change to require screening of all baggage, thepercentage may be increased to unity, or 100%.

The sub-steps in the baggage screening during check-in step 14 may alsobe separately modeled. For example, the baggage is typically loaded intothe baggage screening device, and the baggage screening device checksthe baggage. The next sub-step depends on whether the baggage screeningdevice sounds an alarm. If the baggage screening device or personnelmanning the device sounds an alarm, the alarm is resolved before thebaggage is cleared for transport, such as a search by hand.

As described in U.S. application Ser. No. 10/293,469, the models 2 and10 may also be used to calculate the effect of policy changes such asestimating the impact of adding another security test or incorporatingdifferent security equipment. Specifically, the model supports datamodeling and simulation by provided quantitative modeling support andanalysis to develop fact-based recommendations for policy decisions. Forexample, the model 10 may be used to simulate checkpoint staffingrequirements such as a required number of wanders, bag searchers, etc.for various checkpoint configurations. The model 10 may also be used tosimulate checkpoint equipment requirements, such a required number ofX-Rays machines for various station configurations. The model 10 mayfurther be used to recommend checkpoint staffing for peak volume andnon-peak operations. Similarly, the model 10 may be used to assess (1)continuous (random) policy compliance levels for security devices; (2)the impact of alternative, gender based scanning policies; (3) theimpact of eliminating or adding various screening steps in the securitycheckpoint; (4) the impact of check-in counter wait time on securitycheckpoint demand; or (5) the impact of reduced station staffing oncheckpoint operations.

The data modeling provides analytical support for security checkpointoperations focusing on resources requirements (equipment & staffing),process performance, customer experience and cost. For instance, themodel 10 may be modified to provide analytical support for variousresource requirement policy concerns such as: Employee work rules(impact of number of breaks, lunch, training etc.); reduced checkpointstaffing requirements (impacts of reduced staff on checkpointoperations); reduced airport staffing requirements (optimized schedulingof shared resources across airport); new staffing requirements based onprocess changes (i.e. checkpoint selectee screening); or annual laborplanning based on seasonal demand (Workforce management on annualbasis). Specifically, the addition/subtraction of requirements in acheckpoint may be modeled through the addition/elimination of substepsin the model 10.

By varying the values in the model 10, the model 10 further providesanalytic support for various checkpoint process change policies concernssuch as: Process changes or re-designs (i.e. new security directiveswhich change process steps or time); new technology inserted into theexisting or redesigned process (i.e. new type of x-ray); or emergencyresponse planning (concourse dumps, checkpoint shutdowns, etc.).Specifically, these process changes refer to modification of processesalready included in a model 10.

The present invention may also provide analytic support for variouscustomer experience policy concerns such as: alternative service levelrequirements (i.e. different service levels for non-peak operations);alternative queue management techniques (i.e. “show times” forpassengers); or designated and dedicated stations and lines (i.e.designated stations for premium customers). As described in greaterdetail below, the present invention works by modeling the securitycheckpoint and then specifying a range of values the number of openstations) that results in acceptable customer experiences. Generally, torepresent the changes in customer experience policy concerns, the set ofacceptable ranges is modified as needed to achieve the new standards forcustomer experiences.

As described above, the model produced in step 420 may be used todetermine the impact of changing the number of stations. Using thismodel, a decision maker may determine the number of stations needed atthe security checkpoint at various different times, step 430. Likewise,the model may be used to allocate security machinery at the checkpoint.These decisions are typically made to achieve various performancemeasures of the security checkpoint, and the desired number of stationswill be the smallest number needed to achieve the desired performancemeasure. For example, the security checkpoint may have a maximum desiredwait time (such as 10 minutes) during peak periods on average or busydays, and the effective work schedule staffs the number of stations asneeded to achieve this wait time during different time periods. In thisway, this demand data is then used to determine the number of neededstations, step 430.

Turning now to FIG. 7A, an optimal open station curve 700 is anexemplary visual display that illustrates the optimal number of openstations and how this optimal number of stations varies at differenttimes. As explained above, the optimal number of open stations varieswith demand at the checkpoint. Thus, the optimal open station chart 700in FIG. 7A corresponds with the total demand curve 600 of FIG. 6A. Inthe illustrated open station curve 700, the needed number of openstations peaks at 6 AM and 6 PM. If the depicted open station curve 700represents a security checkpoint at an airport, the demand peakscorrespond to peak travel times (or immediately preceding time periods).Alternatively, the open station curve 700 may represent security demandat a public venue hosting events at 6 AM and 6 PM.

The number of open stations may be rounded up to the nearest wholenumber, thereby ensuring an adequate number of open stations toaccommodate the security demand. In a different embodiment, a partialopen station may represent a partially staffed station that operatessuboptimally but as needed to meet the security demand.

It should be appreciated, however, that the needed number of opensecurity stations calculated in step 400 may be determined through otherprocesses. For instance, the number of stations may be empiricallydetermined based upon prior experiences at the security checkpointsusing management skill. Alternatively, the number of open stations maybe calculated arbitrarily.

Execution

Returning to FIG. 3, an effective working schedule is defined in step800 using the demand data produced in step 400. Specifically, the demandforecasted in step 400 indicates the number of open stations needed toattain various performance measures. However, the demand forecast doesnot indicate how to staff workers optimally in view of the forecasteddemand. In response, the effective work schedule defined in step 800allocates workers as needed to staff the number of desired stationsdetermined in the step 400. As depicted in FIG. 8A, the process ofdefining an effective work schedule in step 800 generally includesdetermining the desired number of workers in step 810, and creating aneffective schedule in step 820.

As depicted above in FIG. 7A, the number of desired stations may varygreatly between peak and non-peak periods, so the number of employeesshould vary correspondingly. Turning now to FIG. 8B, a first step indetermining the desired number of workers in step 810 is to determinethe minimum number of work hours needed to staff the desired number ofstations, step 811. The number of workers is generally represented inworker-hours, corresponding to the number of workers divided by theduration of the time periods of interest. For instance, if 30worker-hours are required for a 30-minute period, then 60 (or 30÷½)workers are actually required. Thus, the number of needed stations maybe represented in worker hours, as depicted in needed worker hour curve710 in FIG. 7B. Worker hour curve 710 corresponds to open station curve700 in FIG. 7A. In particular, as described above in FIGS. 1A and 1B andthe accompanying text, the number of workers has a linear relationshipto the number of open stations. For instance, where there are fiveworkers per open station, then the total number of workers needed at aparticular time equals five times the number of open stations at thattime. Obviously, step 811 may easily adjust for other relationshipsbetween the number of open stations and the number of needed workers.For instance, some security stations are configured such that problemsidentified in a first station are addressed at a second station. In thatinstance, the number of workers is then a function of two or morestations such as requiring nine workers for each pair of securitystations.

Continuing with FIG. 8B, a next step in determining the number ofworkers in step 810 is to define the number of full-time workers and tospecify the condition of work for these workers (e.g., duration andfrequency of work time, as well as conditions for overtime), step 812.Similarly, another task is to decide the number of part-time workers andthe conditions for these employees, step 813. The number of part-timeworkers may be measured as a fraction of the number of full-time workersspecified in step 812. The definition of the workers in steps 812 and813 are described in greater detail below.

Returning to FIG. 8A, an effective schedule is formed in step 820 usingthe worker data from steps 811, 812, and 813. In the field of employeestaffing and scheduling, several techniques are known to create anoptimized schedule using the worker data, such as the informationdescribed above in steps 811, 812, and 813. For instance, an optimizedschedule for a security checkpoint may be formed using linearprogramming, quadratic or mixed-integer programming, nonlinearoptimization, global optimization, non-smooth optimization using geneticand evolutionary algorithms, and constraint programming methods fromartificial intelligence.

In accordance with a preferred embodiment of the present invention, asdescribed below, the effective schedule may be formed in step 820 usinglinear programming to optimize a chosen value (such as minimizing laborcosts or the number of work hours) according to a series of equationsrepresenting to optimize the number of employees, the condition of workfor these employees, and the desired scheduled of employees needed, asdepicted in FIG. 7B. Linear programming is a proven optimizationtechnique. To optimally match employees working with employees neededover the course of a week, all feasible work tours are explicitlyenumerated, and then employees are assigned to these tours. A tour isdefined as a set of shifts that an employee works in a single week. Theformulation of the scheduling problem is therefore a linear programmingproblem of the form:

A·x≧b   Eq. 1

where A is a coefficient matrix, x is a tour assignment matrix, and b isthe demand matrix. A quantifies the condition of employment for securityworkers. The column matrix x corresponds to the work schedule beingcreated. The column matrix b quantifies the minimum staffing at thesecurity checkpoint needed to satisfy demand, as defined above.

The dimensions of the matrixes A, x, and b are [m×n], [n×l], and [m×l],respectively. The variable m represents the number of intervals in atime period being scheduled (such as a week), and the value of m dependson the chosen time interval and time period. For example, if the timeinterval is one hour and the time period equals a week, then m equals168 (twenty-four hours per day multiplied by seven days per week). Ifthe time interval is fifteen minutes, m equals 672 (four fifteen minuteintervals per hour multiplied by twenty-four hours per day multiplied byseven days per week). The total number of feasible tours is n. The valueof n can vary greatly based on the precision of the time interval, aswell the as the number and type of constraints placed on tours. Suchconstraints will be further discussed in regards to the coefficientmatrix A.

Expanding the matrices in Eq. 1, the scheduling formulation looks like:

$\begin{matrix}{{{\begin{bmatrix}a_{11} & a_{12} & a_{13} & \ldots & \ldots & a_{1n} \\a_{21} & a_{22} & a_{23} & \; & \; & \vdots \\a_{31} & a_{32} & a_{33} & \; & \; & \vdots \\\vdots & \; & \; & \; & \; & \vdots \\\vdots & \; & \; & \; & \; & \vdots \\a_{m\; 1} & \ldots & \ldots & \ldots & \ldots & a_{mn}\end{bmatrix} \cdot \begin{bmatrix}x_{1} \\x_{2} \\\vdots \\\vdots \\x_{n}\end{bmatrix}} \geq \begin{bmatrix}b_{1} \\b_{2} \\b_{3} \\\vdots \\\vdots \\b_{m}\end{bmatrix}},{So},\begin{matrix}{{{a_{11}x_{1}} + {a_{12}x_{2}} + {a_{13}x_{3}} + \ldots + {a_{1n}x_{n}}} \geq b_{1}} \\{{{a_{21}x_{1}} + {a_{22}x_{2}} + {a_{23}x_{3}} + \ldots + {a_{2n}x_{n}}} \geq b_{2}} \\\ldots \\\ldots \\{{{a_{m\; 1}x_{1}} + {a_{m\; 2}x_{2}} + {a_{m\; 3}x_{3}} + \ldots + {a_{mn}x_{n}}} \geq b_{m}}\end{matrix}} & {{Eq}.\mspace{14mu} 1}\end{matrix}$

This is the set of linear equations that is optimized to determine adesirable matrix x used to create the effective work schedule for thesecurity checkpoint in step 820.

A process for creating an effective work schedule in step 820 isdepicted in FIG. 8C. Specifically, the creation of an effective workschedule in step 820 includes the steps of determining the demand matrixb in step 821; determining the coefficient matrix A in step 822; andcalculating the scheduling matrix in step 823 using the demand matrix band the coefficient matrix A.

As suggested above, the demand matrix b determined in step 821quantifies the minimum staffing at the security checkpoint needed tosatisfy demand at the security checkpoint.

Specifically, the demand matrix b represents the minimum number ofemployees required to work at time interval i in order to meet passengerdemand for security screening. Demand at interval i is calculated bymultiplying the minimum number of security stations required at thattime by the number of employees needed to operate a station. The stationrequirement can be the result of a spreadsheet model of passengerarrivals and a simulation model of checkpoint operation. In the contextof stations in an airport security checkpoint, the demand matrix b issensitive to flight schedules, load factors, transfer rates, passengerarrival distributions, passenger check-in statistics, and the processingcharacteristics of a station, as described above in the determination ofdemand data in step 400. An exemplary demand matrix b looks like:

$\quad \begin{bmatrix}6 \\18 \\36 \\48 \\\vdots \\\vdots \\12 \\0\end{bmatrix}$

Each of the values in the demand matrix b represents the minimum numberof workers needed at the security checkpoint during the m intervals. Thelarger values correspond to peak periods of demand that therebynecessitate higher staffing levels to meet the demand.

Returning to FIG. 8C, the next action is to determining the coefficientmatrix A, step 822. As described above, the coefficient matrix Aquantifies the condition of employment for security workers.Specifically, the coefficient matrix A indicates if a tour j is workingduring a given time interval i. In the context of this application, atour refers to a series of shifts per employee, per staffing period. Ifa tour is working, a_(ij) equals unity (1). If a tour is not working,a_(ij) equals zero (0).

Scheduling rules and policies dictate the total number of feasibletours, and consequently, the size of the n dimension of the coefficientmatrix A. In general, reducing the number of possible tours (i.e.,reducing the decision space) causes the coefficient matrix A to besmaller and expedites the linear programming calculations used to find asolution for an effective security schedule. Table 1 provides examplesof requirements that determine the size and values of the coefficientmatrix A.

TABLE 1 Possible Tour Policies/Requirements General RequirementDefinition Specific Examples Tour Duration Length of time before 1 week1 month tour schedule repeats Days Off Number of days off 2 days off perweek 2 consecutive days during a tour and when off per week those daysoff occur Shift Assignments to Rules dictating how All shifts in a tourstart at the same time Tours shifts are assigned to All shifts in a tourare the same duration tours Tours can have a combination of shift starttimes and/or durations Shift Start Time Earliest or latest time of day ashift may begin Start time intervals No shifts begin after 22:00 Allshifts start on the hour Shift Duration Maximum and minimum 4 hoursminimum shift, 12 hours bounds on shift length maximum shift Break TimeDuration and time of Breaks occur 2 hours into the shift, and 6 breakshours into shift 15-minute break length Lunch Time Duration and time ofLunch occurs halfway into the shift 30- lunch minute lunch lengthOnce the tour requirements and time interval have been set, thecoefficient matrix A can be generated manually or by using an algorithm.The creation of the coefficient matrix A is a well-know process in thefield of linear programming.

As a simple example, assume that the time interval is eight hours. Alsoassume that tours are weekly, include only shifts with the same starttime and duration, and must give employees two consecutive days off.Finally, assume all shifts are eight hours and begin at 0:00, 9:00, and17:00. The coefficient matrix A is then [21×21], meaning there are 21total time intervals and 21 possible tours. An exemplary [21×10] portionof the matrix looks like:

$\begin{matrix}{Mon} \\\; \\\; \\{Tues} \\\; \\\; \\{Wed} \\\; \\\; \\{Thurs} \\\; \\\; \\{Fri} \\\; \\\; \\{Sat} \\\; \\\; \\{Sun} \\\; \\\;\end{matrix}\begin{bmatrix}1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 \\0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 \\0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 \\1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 \\0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 \\0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 \\1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 \\0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 \\0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 \\0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 \\0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\end{bmatrix}$

Looking at this exemplary coefficient matrix, each of the verticalcolumns represents a unique tour. Each tour consists of a singleeight-hour shift worked on five consecutive days, and two consecutivedays off.

The coefficient matrix A created in step 822 may be used to definevarious staffing conditions. For example, the coefficient matrix A maydefine tours for part-time workers. The coefficient matrix A may alsodefine conditions of employment, such as mandatory breaks; for instance,an additional set of entries may be created for a “break station,” andeach employee may be required to spend a certain amount of time in thebreak station. Similarly, a maximum shift may be defined by subdividingshifts into intervals and preventing shifts that exceed a predefined sumof intervals.

In a simple example, the rows of the matrix indicate 8 hours of work bya person assigned to the tour. Each column represents a tour. A “1”placed in the appropriate row indicates that a worker is assigned to aparticular associated 8 hour shift. If mandatory breaks or lunches mustbe included in the model then the definition of the row can be changedfrom 8 hour to 1 hour, 30 minutes or 15 minutes intervals. Again, as isthe simple example, a value of “1” in a particular location representsthat a worker associated with that location in the matrix is workingduring the interval associated with that location. In contrast, a “0”represents that the worker is not working during that period. To captureperiods when workers are not working, such lunches (30 minutes) andbreaks (15 minutes), rows must be defined as 15 minutes. Corresponding,additional rows are added to represent to increased number of periods.In the simple example, there are 21 rows (3 shifts per day times 7days). By redefining the minimum time interval in the model to 15minutes the new matrix would require 672 rows instead of 21 (3 shifts×8hours×4 time intervals per hour×7 days). In the new matrix that includeslunches and breaks in addition to days off, values of “1” represent whenworkers are working and values of “0” now represent periods when workersare not working, either because the worker is on lunch, break or notscheduled. Typically, all combinations of tour possibilities are builtinto the matrix (more columns would be added to the simple example). Theoptimization calculation would then choose the set of tours thatminimize the objective function.

In the same way, the coefficient matrix A may be used to explain how todefine policy changes such as the use of part time employees, theallowing of full time employees to work non-traditional shifts (e.g.,different from standard eight-hour shifts), the cross-training ofbaggage and passenger screeners, or the cross-training of supervisors toallow for scheduling down/up. The following policy changes may also beaddressed through manipulations of the coefficient matrix A using knownlinear programming techniques:

-   -   Provide eight hour shifts/five days per week per full-time        employee;    -   Provide two consecutive days off;    -   Use overtime intelligently (four 10 hour days/week);    -   Assure male/female ratio allows for same-sex wanding;    -   Provide 30 minute rotation through X-Ray machine;    -   Provide 30 minutes to screen a flight;    -   Allow one hour for daily breaks and lunch;    -   Consider employee constraints (medical, family, religious);    -   Allow screeners to rotate across checkpoints/concourses/baggage        screening functions;    -   Rotate days off to assure all screeners have the opportunity at        weekends off, vacation time;    -   Adapt workforce to accommodate seasonal differences in passenger        flow;    -   Consider training constraints;    -   Incorporate “Fast-Pass” queuing; and    -   Consider employee qualifications for different jobs.        Note that policy changes related to scheduling are addressed        through changes to the coefficient matrix A.

Returning to FIG. 8C, the tour assignment matrix is calculated in step823 from the demand matrix b and the coefficient matrix A defined,respectively, in steps 821 and 822. The tour assignment matrix xindicates the integer number of workers assigned to each of the j tours.The solution will look something like:

$\quad\begin{bmatrix}0 \\2 \\5 \\0 \\0 \\\vdots \\\vdots \\1\end{bmatrix}$

In this example of the tour assignment matrix x, there are no workersscheduled for the first tour, two employees assigned to the second tour,five employees assigned to the third tour, and an employee assigned tothe nth tour.

Typically, the coefficient matrix A and the demand matrix b may beprogrammed into a spreadsheet or mathematical calculation program thatcan automatically solve the linear program. The tour assignment matrix xis optimally determined. Specifically, the matrix x may be determinedusing shift optimization to minimize the total number of hours worked ina week and to create the optimal number of shifts required to operatestations (using a defined mix of full and part-time employees). Itshould be appreciated that the tour assignment x may not be unique inthat several possible tour assignments may provide desirable results. Inthis way, a particular tour assignment x may not be the best but,rather, provides a feasible staffing schedule that meets the forecasteddemand levels. Furthermore, there may be no possible solution to thetour assignment x, indicating the need to make changes to the checkpointor the workforce (e.g., hiring additional workers).

If desired, the number of unique tours used in the solution can belimited by constraining the number of x_(j) that are greater than zero.As described above, reducing the decision space expedites the linearprogramming calculations used to find a solution for an effectivesecurity schedule.

In another embodiment of the present invention, the coefficient matrixmay be used to staff according to specific job skills and training Forinstance, the coefficient matrix may be expanded to include limitationsthat require certain position to be filled only by certain employees.Alternatively, a separate coefficient matrix may be formed for eachposition, and the separate coefficient matrix includes variableslimiting of potential security staffing combinations.

Execution

Returning to FIG. 3, after the schedule is defined in step 800, theschedule is implemented and studied in step 310. In particular, theperformance measures, such as the average and maximum wait time forpeople passing through the checkpoint may be measured. The results ofthe scheduling can be studied and this data may be used to modify thedemand data and to create an associated schedule, step 320. Forinstance, the assumptions used to forecast the required number ofemployees may be modified according to actual events.

In a preferred embodiment of the present invention, the demand dataforecasted in step 400 is re-forecasted intermittently (such asbiweekly, monthly or quarterly) because airport usage, and thus demand,typically varies over an extended period. Then the effective staffingdefined in step in 800 is revised to reflect the seasonal changes in thedemand data.

System

Referring now to FIG. 9, another embodiment of the present inventionprovides an effective security scheduling system 900. As depicted inFIG. 9, the effective security scheduling system 900 generally includesseparate modules that are interconnected to implement the steps in theeffective security scheduling method 300. Specifically, the effectivesecurity scheduling system 900 includes a security demand forecastingmodule 910. The security demand forecasting modeling module 910 acceptsinput data related to the facility. For instance, security demand at anairport may be forecasted using flight schedules, flight capacity data,and predetermined demand distribution curves, as described above.

The effective security scheduling system 900 may further include acheckpoint simulation module 920 using security checkpoint data and thesecurity demand, as described above in FIGS. 4C and 4D and theassociated text. The checkpoint simulation module 920 forecasts thenumber of security workers to be staffed at various times. A simulationmay be performed using a commercially available process simulationsoftware, such as Arena® marketed by Rockwell Software of Sewickley, Pa.Alternatively, the security checkpoint data and the security checkpointdemand may be used by a simulation program created using knownprogramming techniques and known computer languages such as JAVA,Simscript, SLAMII, Extend, Promodel, etc.

Continuing with FIG. 9, a schedule defining module 930 uses user-definedinputs and the outputs from the demand forecasting module 910 and thecheckpoint simulation module 920 to create a security work schedule. Asdescribed above in FIG. 8B and the associated text, the user-definedinputs generally include data related to the number security workers andthe condition of work for these workers. This type of informationincludes the shift length, possible starting and end times, shiftfrequency, breaks, etc. associated with each of the workers.Furthermore, the user-defined inputs may include constraints limitingpotential staffing configurations, such as limiting the staffing ofcertain positions to workers with sufficient employees.

The schedule defining module 930 is typically some type of computerapplication that automatically creates an optimized work schedule usingvarious optimization techniques. For instance, the schedule definingmodule 930 may solve the linear system described above associated withEquation 1. Many commercial spreadsheet applications, such as Excel®produced by Microsoft, Inc. of Redmond, Wash., have optional add-onapplications that can be used to perform these types of optimizations.For instance, Frontline Systems, Inc. of Incline Village, Nev. markets aspreadsheet solver application under the name Premium Solver that workswith a spreadsheet application to quickly solve large optimizationproblems.

Furthermore, the commercial spreadsheet applications may be programmedto automatically accept user-defined inputs from a database and thesecurity demand modeling data from the security demand forecastingmodule 910 and the checkpoint simulation module 920. The commercialspreadsheet applications can then be configured to automatically createthe matrix of interest, as described in Equation 1, and solve thesesystems of the equations. Alternatively, other software, including asmathematical computational application such Mathematica WolframResearch, Inc. of Champaign, Ill. may automatically solve matricesmanually created by a user from the input data and the security demandmodeling data.

Continuing with FIG. 9, after the schedule is created by the scheduledefining module 930, a schedule implementation module 940 may optionallyprovide the schedule to the security workers using known means. Forinstance, the schedule implementation module 940 may electronicallypresent the schedule to the security workers. The scheduleimplementation module 940 may further track the activities of workers toensure adherence with the schedule. For instance, the scheduleimplementation module 940 may record the actual start and end times forworkers, along with any break times in between. This may beautomatically accomplished by requiring workers to electronicallyregister when entering or leaving the security checkpoint.

Returning to FIG. 9, the adjustment module 950 collects data regardingthe operation of the security checkpoint and uses this data to adjustthe operations of the other modules in the effective security schedulingsystem 900. For instance, the adjustment module 950 may alter theassumptions used by the security demand modeling module if properimplementation of the schedule has undesirable effects, such asexcessive wait times. Similarly, the adjustment module 950 may suggestchanges in the operation of the schedule defining module 930, such asthe hiring of additional workers or additional types of workers, asneeded to produce more effective schedules in view of the securitydemand model. In the same way, changes may be made to the scheduleimplementation module 940 where workers are not complying with theschedule created by the schedule defining module 930.

The adjustment module 950 may also be used by management and employeesto adjust the schedule as needed. For example, the adjustment module 950may accept feedback from workers to adjust the schedule, such asrequests for vacation days or requests for schedule changes. Similarly,management may add additional requirements, such as additionaladministrative time for the employees. For instance, the workers may berequired to attend training or administrative meetings. The effectivesecurity scheduling system 900 may then schedule these administrativetasks during periods of excess labor capacity, when the checkpoint canspare the loss of some workers without adverse effect to the performancemeasures.

Extended Planning

In another embodiment, the ESS method 300 and the system 900 may be usedto forecast security staffing needs over an extended period and to staffsecurity personnel accordingly. Often, locations have security screeningdemand that varies over an extended period, such as over) differentmonths, seasons or years. Thus, the number of needed security personnelfor an efficient schedule, as described above, may similarly vary overthe extended period as needed to meet the change in demand. For example,FIG. 10 depicts an extended needed worker graph 1000 that represents alocation that needs more security workers over the summer.

The changes in the needed number of workers over an extended period maybe predicted through forecasting the needed number of security stationsin step 400 and defining an effective schedule in step 800, both overthe extended period of interest. For instance, needed number of securitystations at an airport may be forecasted over an extended period to formthe extended needed worker graph 1000 by examining the number of flightsdeparting from the airport, the load factors for these flights, etc. asdescribed above in FIG. 4B and the associated text.

In general, the extended forecast is performed at prespecifiedintervals. For example, a security checkpoint may be modeled and anoptimized schedule formed once, every three months to determine seasonalchanges in demand and resulting changes to staffing needs.

Security checkpoint management may then match seasonal demandfluctuations using hiring lead times and managed attrition to enablestaffing over an extended period as needed to meet desired servicelevels. For instance, referring back to the extended needed worker graph1000 in FIG. 10, the employees need to be hired and trained prior to thepeak summer period. Similarly, by forecasting the decrease in securityscreening demand that occurs after the summer season, management mayappropriately cease hiring and allow the staff size to decrease asneeded through natural attrition.

By staffing over an extended period, the present invention may reducecosts associated with hiring new personnel. For instance, an extendedstaffing schedule may include an integrated training plan that reducesthe number of training sessions required. Likewise, staffing over anextended period may reduce procurement cost through increased employerbargaining power gained through an integrated supply plan. As a result,the present invention allows managers to analyze policy change impactsprior to and during a budget process and to proactively build responseto budgetary impacts into a strategic plan.

When forecasting and staffing over an extended period, such as theextended needed worker graph 1000, various planning assumptions andfactors may be used. For instance, the forecast may include data relatedto industry growth trends, individual checkpoints, and other factors.Likewise, staffing over an extended period may consider historic demandpatterns, historic staffing requirements, and individual checkpointcharacteristics. The extended forecasts and staffing may furtherimplement policies that may not effect short-term staffing, includingsecurity directives, staffing rules, and policy changes.

Without the present invention, security staffing and hiring arecurrently reactionary (i.e., ad hoc) in that management responds to highdemand and long delays (or other undesirable service measures) by hiringadditional workers. However, hiring new workers is often not effectivein the short-run to address high demand. Hiring is a potentiallyextended process. Furthermore, new employees must be adequately trained,further stressing the existing supply of labor resources. The hiring andtraining of new employees is also a potentially expensive process if themanagement must compete with other employers for scarce employees duringpeak periods.

In response, the managers may use the present invention to createpolicies for cost effective mixes of full time, part time, and termemployees. Using part time and term employees to augment full-timeemployees is generally an effective approach to manage seasonal peaksefficiently. By forecasting demand over an extended period, managers ofa security checkpoint may hire and train the part time and termemployees before the peak periods in order to reduce the duration ofovercapacity periods. An employee mix may be used to decrease thetraditional time required to meet seasonal peaks. Likewise, an employeemix may be used to decrease the time required to return to baselineworkforce. By anticipating the peak demand periods, management mayidentify labor pools that are readily available on part time or termbasis. Alternatively, demand peaks may be addressed through the creationof regional workforce teams with the capability to move betweendifferent security checkpoint locations based on demand. Furthermore,the managers may allocate assets (i.e., the machinery used in thecheckpoint) based on the characteristics of the asset and fieldrequirements.

Thus, it can be seen that the present invention enables a labor planningprocess to determine resource levels required for the upcoming timeframe based on demand forecasting methodology and effective scheduling.Management may then determine resources required to meet service levels.Consequently, the present invention allows management to understand theimpacts of resource allocation prior to the beginning of budget cycles.

Policy Analysis

As described above in FIG. 4A and the accompanying text, policy changesto the security checkpoint may be reliably simulated by modifying themodel of the security checkpoint. The modified model may then be used todetermine changes in the number of workers needed to achieve acceptableperformance measures. A new optimized schedule may be formed for thisnew number of workers. The cost of the policy changes is then theadditional costs from the new optimized schedule, in comparison to anexisting schedule. For instance, this type of policy analysis maypredict the change labor costs associated with new machine or additionalstep in the checkpoint.

In contrast, a change in staffing policy may be evaluated by changingthe constraints used to form an optimized schedule, as described in FIG.8A and the accompanying text. Referring to the linear programming methoddescribed above, the coefficient matrix A may be modified to reflect newstaffing policies that place additional requirements on schedulecreation.

Conclusion

A schedule created by the present invention results in great savings inlabor costs and ensuring that desired service levels are met. Asdepicted in FIG. 2C, an optimized scheduling chart 200″ illustrates howan effective work schedule staffs the security checkpoint as needed tomeet demand at the checkpoint to reduce inefficient labor allocation andunnecessary labor costs. In comparison to the scheduling charts 200 and200′, depicted in FIGS. 2A and 2B respectively, the optimized schedulingchart 200″ of FIG. 2C graphically depicts how varying the staffing atthe security checkpoint in accordance with demand at the checkpointresults In much less overstaffing. In particular, there is much lessdifference between the number of required employees line 210″, and thenumber of employees working line 220″. Moreover, the number of employeesgenerally meets or exceeds the staffing levels needed to accommodate theinstantaneous demand for security. Accordingly, the optimized workschedule, as embodied by chart 200″ depicts improvements in efficiencyproduced by the present invention.

As described above, the number of needed workers represents theworkforce needed to satisfy various performance measures. In this way,the forecasted demand for security screening may exceed capacity of thestaffed number of workers, but the effects of the excess demand shouldstill fall within the desired performance measures (e.g., a delay mayresult, but the expected delay should be less than a predefined limit,as estimated in modeling during step 420). Likewise, as described above,the number of needed workers also considers time needed for variousadministrative tasks, including training, supervision, job reviews, etc.In this way, the security checkpoint should be adequately staffed asneeded to achieve the performance measures, even if the some of theemployees are required to participate in administrative tasks.

Thus, the present invention may be used to achieve numerous desiredresults, including lower total personnel costs; reduced numbers offull-time employees (FTE); greater diversity in the workforce (throughthe use of part-time or seasonal employees); improved cost effectivenesswhile at least maintaining the customer service level; the creation ofconsistency in staffing and scheduling; the development of rule-driven,repeatable schedules; maximizing employee morale; reducing costsassociated with scheduling; reducing the costs of creating andmaintaining schedule; better support of security efforts and operationof the Federal Security Director; iteration to address problems asrequired; and sufficient flexibility to respond to potential policychanges.

The foregoing description of the preferred embodiments of the inventionhas been presented for the purposes of illustration and description. Itis not intended to be exhaustive or to limit the invention to theprecise form disclosed. Many modifications and variations are possiblein light of the above teaching. It is intended that the scope of theinvention be limited not by this detailed description, but rather by theclaims appended hereto. The above specification, examples and dataprovide a complete description of the manufacture and use of thecomposition of the invention. Since many embodiments of the inventioncan be made without departing from the spirit and scope of theinvention, the invention resides in the claims hereinafter appended.

1. A method for forming a schedule for security workers, the methodcomprising the steps of: forecasting demand for a security checkpointover a first period of time intervals; determining a needed number ofworkers during each of the intervals to meet the forecasted demand,wherein the determining the needed number of workers comprises modelingthe security checkpoint at each of the time intervals in view of theforecasted demand, wherein the modeling evaluates the securitycheckpoint with different numbers of workers, and selecting the numberof workers for each time interval as needed to achieve a performancemeasure, wherein the security checkpoint separately models two or moretasks in the security checkpoint with results from at least one of theseparate models varying with changing numbers of workers; and schedulingat least said needed number of workers to each of the intervals. 2-45.(canceled)